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BEAT-CF: Bayesian Evidence-Adaptive Tool to optimise management of Cystic Fibrosis

An innovative response-adaptive approach to driving improvements in health outcomes, applied to cystic fibrosis

Investigators: Andre Schultz, Anne McKenzie, Carly McCallum, Grace Currie, Julie Marsh, Mitch Messer, Stephen Stick

External collaborators: Scott Berry (Berry Consultants), Claire Wainwright (Lady Cilento Children's Hospital), Ben Saville (Berry Consultants), Hugh Greville (Royal Adelaide Hospital), Jamie Wood (Sir Charles Gairdner Hospital), Peter Middleton (University of Sydney), Scott Bell (Royal Brisbane Women's and Children's Hospital), Susannah Ahern (Monash University), Sarath Ranganathan (Murdoch Children's Research Institute)

The main aim of the study is to provide timely surveillance data to public health authorities on severe influenza. This complements data collected in sentinel general practices on influenza across Australia, and helps public health authorities form a picture of influenza activity.

Viral or bacterial infections can make symptoms of cystic fibrosis (CF) worse, including reducing lung function and shortening life expectancy. Currently there is not enough evidence available to help clinicians chose the correct treatment and better tools are desperately needed to help them prescribe the most effective antibiotics. We aim to build a web-based tool that will use all of a patient's information to recommend the most effective antibiotics. We aim to treat infections in children with CF-based on modelling, rather than guesswork. We aim to include this tool within a clinical trial that will also manage other aspects of CF care including diet, physical therapy, lung function and mental health.